Efficient algorithms for a class of partitioning problems
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Efficient algorithms for a class of partitioning problems

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Published by National Aeronautics and Space Administration, Langley Research Center, National Technical Information Service, distributor in Hampton, Va, [Springfield, Va .
Written in English

Subjects:

  • Electronic data processing -- Distributed processing.,
  • Parallel processing (Electronic computers),
  • Algorithms.,
  • Partitions (Mathematics)

Book details:

Edition Notes

StatementM. Ashraf Iqbal, Shahid H. Bokhari.
SeriesICASE report -- no. 90-49., NASA contractor report ; 182073, NASA contractor report -- NASA CR-182073.
ContributionsBohkari, Shahid H., Langley Research Center.
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL15396450M

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  Abstract. Motivated by an expensive computation performed by a computational topology software RIVET [], Madkour et al. [] introduced and studied the following graph partitioning an edge weighted graph and an integer k, partition the vertex set of the graph into k connected components such that the weight of the heaviest component is as small as possible, where Cited by: 3. - 1 there is a weight given by a real number ci*ir+I. We are interested in efficient algorithms for solving the following two path partitioning problems. Problem A. Find an integer 1 partitioning of 1,2,, n into p sub- sequences such that D is minimized. Product Information: Excerpt from Partitioning Algorithms for a Class of Knapsack Problems In this paper algorithms are presented for evaluating the knapsack function for a class of two-dimensional knapsack problems such as arises, for example, in the solution of cutting stock problems in staged guillotine cutting Rating: % positive. The beginning algorist might suggest a heuristic as the most natural approach to solve the partition problem. Perhaps they would compute the average size of a partition,, and then try to insert the dividers so as to come close to this r, such heuristic methods are doomed to fail on certain inputs, because they do not systematically evaluate all possibilities.

  Abstract. The k-Min-Cut (k-Max-Cut) problem consists of partitioning the vertices of an edge weighted (undirected) graph into k sets so as to minimize (maximize) the sum of the weights of the edges joining vertices in different subsets. We concentrate on the k-Max-Cut and k-Min-Cut problems defined over complete graphs that satisfy the triangle inequality, as well as on d-dimensional graphs.   The problem has been studied in the literature as the “chains-on-chains partitioning” problem. Despite the rich literature on exact algorithms, heuristics are still used in parallel computing community with the “hope” of good decompositions and the “myth” of exact algorithms being hard to implement and not runtime efficient. This problem is known as balanced partition problem. For example, Input: A = [1,7,4,11], Output: 1 Explanation: Two subsets can be: {1,11} and {7,4}, two have a difference of 1, which is the minimum difference we can get by splitting this array. Class time and Location: MW , Building Terman, Room Algorithmic approaches to graph partitioning problems: Background and overview for graph partitioning: , "Four algorithms for the efficient computation of truncated QR approximations to a sparse matrix" Berry, Pulatova, and Stewart, "Computing Sparse Reduced-Rank.

  Lectures. This page provides information about online lectures and lecture slides for use in teaching and learning from the book Algorithms, 4/ lectures are appropriate for use by instructors as the basis for a “flipped” class on the subject, or for self-study by individuals. Interval partitioning problem. In continuation of greedy algorithm problem, (earlier we discussed: even scheduling and coin change problems) we will discuss another problem m is known as interval partitioning problem and it goes like: There are n lectures to be schedules and there are certain number of classrooms. Each lecture has a start time s i and finish time f i. To benefit from this handbook, some familiarity with approximation algorithms and metaheuristics is required. The first part of the handbook deals with basic methodologies to design and analyze efficient approximation algorithms for a large class of problems and to establish inapproximability results for other classes of problems. Quick Search in Books. Enter words / phrases / DOI / ISBN / keywords / authors / etc. Search. Quick Search anywhere. Enter words / phrases / DOI / ISBN / keywords / authors / etc. Search. Quick search in Citations. Journal Year Volume Issue Page. Search. Advanced .